Object contour and occlusion detection is a fundamental problem for numerous vision tasks, including image segmentation, object detection, semantic instance segmentation, and occlusion reasoning. Detecting all objects in a traffic environment, such as cars, buses, pedestrians, and bicycles, is crucial for building an autonomous driving system. Failure to detect an object (e.g., a car or a person) may lead to malfunction of the motion planning module of an autonomous driving car, thus resulting in a catastrophic accident. As such, object occlusion detection for autonomous vehicles is an important safety issue.
There are various states of occlusion. Identifying specific types of occlusion can facilitate the process of object occlusion detection for autonomous vehicles. The most common types of object occlusion are: one object occluding another object, one object is occluded by another object, one object is between two other objects, and an object is separated from other objects. For autonomous vehicle occlusion detection, the major types of objects that need to be detected are cars, motorcycles, bicycles, persons, and the like. Accurately distinguishing the relationship among objects around a host autonomous vehicle provides valuable information for motion planning, driving inference generation, and other processes of the autonomous vehicle operation.
Object contour and occlusion detection can involve the use of semantic segmentation. Semantic segmentation aims to assign a categorical label to every pixel in an image, which plays an important role in image analysis and self-driving systems. The semantic segmentation framework provides pixel-level categorical labeling, but no single object-level instance can be discovered. Current object detection frameworks, although useful, cannot recover the shape of the object or deal with the occluded object detection problem. A more accurate and efficient detection of object occlusion is needed for autonomous vehicle operation.